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my_model_selectors.py
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my_model_selectors.py
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import math
import statistics
import warnings
import numpy as np
from hmmlearn.hmm import GaussianHMM
from sklearn.model_selection import KFold
from asl_utils import combine_sequences
class ModelSelector(object):
'''
base class for model selection (strategy design pattern)
'''
def __init__(self, all_word_sequences: dict, all_word_Xlengths: dict, this_word: str,
n_constant=3,
min_n_components=2, max_n_components=10,
random_state=14, verbose=False):
self.words = all_word_sequences
self.hwords = all_word_Xlengths
self.sequences = all_word_sequences[this_word]
self.X, self.lengths = all_word_Xlengths[this_word]
self.this_word = this_word
self.n_constant = n_constant
self.min_n_components = min_n_components
self.max_n_components = max_n_components
self.random_state = random_state
self.verbose = verbose
def select(self):
raise NotImplementedError
def base_model(self, num_states):
# with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
# warnings.filterwarnings("ignore", category=RuntimeWarning)
try:
hmm_model = GaussianHMM(n_components=num_states, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(self.X, self.lengths)
if self.verbose:
print("model created for {} with {} states".format(self.this_word, num_states))
return hmm_model
except:
if self.verbose:
print("failure on {} with {} states".format(self.this_word, num_states))
return None
class SelectorConstant(ModelSelector):
""" select the model with value self.n_constant
"""
def select(self):
""" select based on n_constant value
:return: GaussianHMM object
"""
best_num_components = self.n_constant
return self.base_model(best_num_components)
class SelectorBIC(ModelSelector):
""" select the model with the lowest Bayesian Information Criterion(BIC) score
http://www2.imm.dtu.dk/courses/02433/doc/ch6_slides.pdf
Bayesian information criteria: BIC = -2 * logL + p * logN
"""
def select(self):
""" select the best model for self.this_word based on
BIC score for n between self.min_n_components and self.max_n_components
:return: GaussianHMM object
"""
warnings.filterwarnings("ignore", category=DeprecationWarning)
best_score = float('inf')
best_model = None
for n in range(self.min_n_components, self.max_n_components+1):
try:
model = GaussianHMM(n_components=n, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(self.X, self.lengths)
logL = model.score(self.X, self.lengths)
# equation from udacity forum: https://discussions.udacity.com/t/how-to-start-coding-the-selectors/476905/10
p = n ** 2 + 2 * n * len(self.X[0]) - 1
N = len(self.X)
score = -2 * logL + p * np.log(N)
if score < best_score:
best_score = score
best_model = model
except:
continue
return best_model
class SelectorDIC(ModelSelector):
''' select best model based on Discriminative Information Criterion
Biem, Alain. "A model selection criterion for classification: Application to hmm topology optimization."
Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on. IEEE, 2003.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.6208&rep=rep1&type=pdf
https://pdfs.semanticscholar.org/ed3d/7c4a5f607201f3848d4c02dd9ba17c791fc2.pdf
DIC = log(P(X(i)) - 1/(M-1)SUM(log(P(X(all but i))
'''
def select(self):
warnings.filterwarnings("ignore", category=DeprecationWarning)
best_score = float("-inf")
best_model = None
for n in range(self.min_n_components, self.max_n_components+1):
try:
other_words_score = 0.0
quantity = 0.0
model = GaussianHMM(n_components=n, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(self.X, self.lengths)
this_word_score = model.score(self.X, self.lengths)
for word in self.hwords:
if word != self.this_word:
quantity += 1
X, lengths = self.hwords[word]
other_words_score += model.score(X, lengths)
# equation from udacity forum: https://discussions.udacity.com/t/how-to-start-coding-the-selectors/476905/10
score = this_word_score - other_words_score / quantity
if score > best_score:
best_score = score
best_model = model
except:
continue
return best_model
class SelectorCV(ModelSelector):
''' select best model based on average log Likelihood of cross-validation folds
'''
def select(self):
warnings.filterwarnings("ignore", category=DeprecationWarning)
best_score = float('-inf')
best_model = None
sum_score = 0.0
counter = 0.0
if len(self.sequences) >= 3:
n_splits = min(3, len(self.sequences))
splits = KFold(n_splits)
for n in range(self.min_n_components, self.max_n_components+1):
try:
for train_index, test_index in splits.split(self.sequences):
# used forum code to get train/test X,Lengths respectively: https://discussions.udacity.com/t/selectorcv-crashes/400125
train_X, train_lengths = combine_sequences(train_index, self.sequences)
test_X, test_lengths = combine_sequences(test_index, self.sequences)
model = GaussianHMM(n_components=n, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(train_X, train_lengths)
score = model.score(test_X, test_lengths)
sum_score += score
counter += 1
# used average score from udacity forum: https://discussions.udacity.com/t/my-selectorcv-class/349110
average_score = sum_score / counter
if average_score > best_score:
best_score = average_score
best_model = model
except:
continue
# for models with length less than 3
else:
best_score_1 = float('inf')
best_model = None
for n in range(self.min_n_components, self.max_n_components+1):
try:
model = GaussianHMM(n_components=n, covariance_type="diag", n_iter=1000,
random_state=self.random_state, verbose=False).fit(self.X, self.lengths)
logL = model.score(self.X, self.lengths)
# equation from udacity forum: https://discussions.udacity.com/t/how-to-start-coding-the-selectors/476905/10
p = n ** 2 + 2 * n * len(self.X[0]) - 1
N = len(self.X)
score_1 = -2 * logL + p * np.log(N)
if score_1 < best_score_1:
best_score_1 = score_1
best_model = model
except:
continue
return best_model